Environ Sci Pollut Res (2015) 22:4297–4304
DOI 10.1007/s11356-014-3655-4
RESEARCH ARTICLE
Derivation of marine water quality criteria for metals based
on a novel QICAR-SSD model
Cheng Chen & Yunsong Mu & Fengchang Wu &
Ruiqing Zhang & Hailei Su & John P. Giesy
Received: 4 March 2014 / Accepted: 24 September 2014 / Published online: 8 October 2014
# Springer-Verlag Berlin Heidelberg 2014
Abstract Establishment of water quality criteria (WQC) is
one procedure for protection of marine organisms and their
ecosystems. This study, which integrated two separate approaches, quantitative ion character–activity relationships
(QICARs) and species sensitivity distributions (SSDs), developed a novel QICAR-SSD model. The QICARs predict relative potencies of individual elements while SSDs integrate
relative sensitivities among organisms. The QICAR-SSD approach was applied to derive saltwater WQC for 34 metals or
metalloids. Relationships between physicochemical properties of metal ions and their corresponding potencies for acute
toxicity to eight selected marine species were determined. The
softness index (σp) exhibited the strongest correlation with the
acute toxicity of metals (r2 >0.66, F>5.88, P<0.94×10−2).
Predictive criteria maximum concentrations for the eight
metals, derived by applying the SSD approach to values
predicted by use of QICARs, were within the same order of
Responsible editor: Philippe Garrigues
Electronic supplementary material The online version of this article
(doi:10.1007/s11356-014-3655-4) contains supplementary material,
which is available to authorized users.
C. Chen : F. Wu (*)
College of Environment, Hohai University, Nanjing 210098, China
e-mail: wufengchang@vip.skleg.cn
C. Chen : Y. Mu : F. Wu : R. Zhang : H. Su
State Key Laboratory of Environmental Criteria and Risk
Assessment, Chinese Research Academy of Environmental
Sciences, Beijing 100012, China
J. P. Giesy
Department of Veterinary Biomedical Sciences, University of
Saskatchewan, Saskatoon, SK, Canada
J. P. Giesy
Department of Zoology, and Center for Integrative Toxicology,
Michigan State University, East Lansing, MI, USA
magnitude as values recommended by the US EPA (2009). In
general, the results support that the QICAR-SSD approach is a
rapid method to estimate WQC for metals for which little or
no information is available for marine organisms.
Keywords Marine water quality criteria . Quantitative ion
character–activity relationship . The softness index . Metals or
metalloids . Species sensitivity distribution
Introduction
Oceans cover approximately 71 % of the Earth’s surface and
form its largest aquatic ecosystem with a great diversity of
marine organisms. Due to industrialization and urbanization,
quantities of metals have been mobilized into the marine
environment, posing significant ecological risks on marine
ecosystems, such as the extinction of some rare species, and
reduction of biodiversity especially in coastal and estuarine
areas (Johnston and Roberts 2009). To limit future contamination while remediating previous effects, countries, including China, are formulating effective protective measures for
protecting the marine environment, and some useful progress
has been made since the early 1960s (Goldberg 1992). Among
the measures, derivation of marine water quality criteria
(WQC) founded on scientifically sound environmental risk
assessment framework is essential for regulating chemical
discharges and water quality management as a tool to enhance
protection of ecosystem structure and function (Leung et al.
2014; Merrington et al. 2014). With numerical WQC, environmental agencies and institutions can mount cost-effective
monitoring programs, enforcing protective measures, designing remedial actions, and closely following international
treaties to control pollution and protect marine biodiversity.
Over the past few decades, some countries, such as the USA,
Australia, New Zealand, Canada, China, and the Netherlands,
4298
and organizations, such as the European Union, have published or revised water quality criteria (US EPA 2009;
ANZECC/ARMCANZ 2000; CCME 2007; Wu et al. 2010;
Meng and Wu 2010; Feng et al. 2012a; Van Vlaardingen and
Verbruggen 2007; ECB 2003). Since values recommended by
US Environmental Protection Agency (US EPA) were taken
as points of reference in our paper, a brief introduction about
the history of marine WQC development in America is given.
The US EPA has established comprehensive systems for
deriving marine WQC and provided guidelines for development of individual national criteria over the past. Until now,
marine WQC have been established for only 10 metals or
metalloids by the US EPA (2009). Development of those
criteria was based on toxicity data obtained from standard
acute toxicity tests with surrogate, saltwater species that
commonly inhabit the marine environment in North
America. Attention has been focused primarily on protection of freshwater organisms in several developed and
developing countries, such as China(Wu et al. 2008,
2011a, b; Feng et al. 2012b). Since fewer standard toxicity
test methods or surrogate saltwater organisms are available, data, especially for toxicity of metals to saltwater
vertebrates, have often been insufficient to meet requirements for developing WQC using conventional approaches
(Merrington et al. 2014). Several studies have attempted to
extrapolate toxicity data from freshwater to saltwater species (Hutchinson and Scholz 1998; Leung et al. 2001;
Wheeler et al. 2002). However, there is currently no
consensus on the reliability of these methods for derivation of WQC for protecting saltwater organisms using
available toxicity data obtained from freshwater organisms.
Quantitative ion character–activity relationships (QICARs)
establish intrinsic relationships between physicochemical properties and biological activities of metal ions, such as toxic
potency to cause adverse effects on survival growth and reproduction. QICARs have been used as a robust statistical approach to develop predictive models of metal toxicity. Using
data on toxicity of metals to the marine luminous bacterium
Vibrio fischeri and the soil nematode Caenorhabditis elegans,
Newman and his colleagues (McCloskey et al. 1996; Newman
et al. 1998; Tatara et al. 1998; Ownby and Newman 2003)
investigated the feasibility of predicting toxicity of metals based
on physicochemical properties of metal ions. They found that
the first hydrolysis constant |log KOH| was strongly correlated
with potency for toxic effects of metal ions (r2 =0.93). Multiple
predictive equations were further established in combination
with data from the US EPA’s ECOTOX database, in which |log
KOH|, together with the softness index σp, exhibited the strongest correlation with toxicities of metals (Ownby and Newman
2003). The softness index σp was found to be an optimal
parameter for predicting toxicity of metals to Tetrapymena
pyriformis. Mercury (Hg) and cadmium (Cd), which tended
to bind with S-containing groups, were the most toxic to
Environ Sci Pollut Res (2015) 22:4297–4304
T. pyriformis (Bogaerts et al. 2001). QICARs have been used
to predict effects of metals for which there were insufficient
data on toxicity to a range of species to predict toxicity potency
among different metals and among different species.
Therefore, this study developed and validated an alternative, novel, QICAR-SSD approach which integrates the
QICARs to predict relative potencies among metals or
metalloids with species sensitivity distributions (SSDs),
which describe relative sensitivities among organisms to
derive saltwater WQC. In accordance with US EPA requirements for derivation of saltwater WQC, eight families
of marine species inhabiting North America were included
in this study and there had to be sufficient toxicity data
for each species. A QICAR model was then developed to
predict acute toxicities of 34 metals or metalloids to
selected sensitive marine species for which data were not
available. The SSD approach, with the goal to protect
95 % of species, was used to predict criteria maximum
concentrations (CMCs) of selected metals or metalloids.
The QICAR-SSD model was then used to derive saltwater
WQC. Comparisons between the predicted values and the
CMCs recommended by the US EPA were also made as a
way for validation of the QICAR-SSD approach.
Methods
Modeling data set
Toxicity data were selected for inclusion by application of
basic requirements for data used in developing WQC,
which have been previously described (Stephan et al.
1985). For instance, under those guidelines, a minimum
of eight species (three phyla) were required. The specific
rules are as follows: (1) the data set must include toxicity
values for at least five metals to the same species, (2) the
species must inhabit North America, (3) the toxicity tests
must strictly follow standard methods, (4) the salinity of
experimental water must be controlled within the range of
20–35‰, and (5) the exposure time of the acute toxicity
test must be 48–96 h. Thus, five phyla and eight families
of model aquatic organisms expected to be sensitive to the
effects of heavy metals, including three Mollusca (Mya
arenaria, Crassostrea virginica, Nassarius obsoletus), two
Arthropoda (Pagurus longicarpus, Mysidopsis bahia), a
Chordata (Fundulus heterolitus), an Annelida (Nereis
virens), and an Echinodermata (Asterias forbesi), were
considered (Calabrese et al. 1973; Eisler and Hennekey
1977; Lussier et al. 1985). Due to the paucity of data on
acute toxicity of metals to marine organisms in the phylum Chordata, a single representative fish, the mummichog
(Fundulus heteroclitus), was selected. The mummichog is
a small euryhaline species that occurs in many places in
Environ Sci Pollut Res (2015) 22:4297–4304
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the world and is often used in toxicity tests. Ecological
classification of selected marine species, including phyla
and species, is presented in Table 1.
Results and discussion
Characteristics of metals and SSD fitting
Correlation analysis between log-LC50 and the 14 characteristics of metal ions demonstrated that the softness index σp is
better correlated with log-LC50 (r2 >0.66). The σp, proposed
based on the hard-soft-acid-base theory, is an evaluation parameter to comprehensively characterize the degree of difficulty for a metal ion to form covalent and ionic bonds (Jones
and Vaughn 1978). Results of previous studies have indicated
a correlation between toxicity and softness index of metal ions
(Turner et al. 1983; McCloskey et al. 1996; Bogaerts et al.
2001). Therefore, here the σp was used to establish QICAR
equations for eight model organisms by use of singleparameter linear regressions (Table 1).
The log-LC50 values of F. heteroclitus and A. forbesi are
significantly positively correlated with σp (r2 =0.89, F=
23.63, P =1.66× 10−2; and r2 =0.88, F = 21.99, P =1.83×
10 − 2 , r espectively). A ccuracy of p rediction f or
F. heteroclitus was greater than that reported by Ownby and
Newman (2003), who retrieved toxicity data pertaining to
F. heteroclitus from the ECOTOX database and examined
the correlation relationship between log-LC50 and |log KOH|
(r2 =0.78, F=14.51, P =1.90×10−2). Log-LC50 values of bivalve mollusks Mya arenaria and C. virginica are also strongly correlated with σp (r2 =0.85, F=17.25, P=2.54×10−2; and
r2 =0.74, F=16.87, P=0.63×10−2), whereas the Gastropoda
Nassarius obsoletus of the same phylum has a P value of
5.60×10−2. The toxicity data pertaining to the arthropod
P. longicarpus and the annelid Nereis virens were weakly
correlated with σp (r2 =0.66, F=5.88, P=9.38×10−2; and
r2 =0.70, F=6.98, P=7.75×10−2, respectively). These three
species have the P value, 0.05<P<0.10, accepted for statistical significance at the 90 % confidence level. Coefficients of
determination (r2) are all greater than 0.66, exhibiting a good
prediction of metal toxicity (Table 1 and Fig. 1). As a reasonable model to predict acute toxicity value from the known
As in previous studies (Wu et al. 2013; Mu et al. 2014),
the following characteristics of metal ions establishing the
QICAR model were considered: softness index, σp; the
largest stability constant of complexes, lgβn; Pauling electronegativity, Xm; covalent index, Xm2r; atomic ionization
potential, AN/ΔIP; the first hydrolysis constant, |log KOH|;
electrochemical potential, ΔE0; atom size, AR/AW; relative softness of the ion, Z/rx (x is electronegativity);
polarizable ability parameters, Z/r, Z/r2, and Z2/R; and
the polarizable ability-like parameters, Z/AR and Z/AR2
(Kaiser 1980; Lide and Haynes 2011; Pearson and Mawby
1967; Baes and Mesmer 1976; Wolterbeek and Verburg
2001).
The most significant characteristics of metal ions were
selected based on correlations between ion characteristics
and toxicity values. Selected ion characteristics were then
used as independent variables, and toxicities of metals
expressed as either 48 or 96 h LC50 were used as the
dependent variables for five phyla and eight families of
species by linear fitting. Predictive capacities of QICAR
models were evaluated by use of the coefficient of determination (r2), residual sum of squares (RSS), F value
using multiple analysis of variance (ANOVA), and the
level of type I error (P).
LC50 values were calculated for each marine species based
on QICAR equations. According to the SSD analysis recommended by the US EPA, these toxicity data were sorted, fitted
by the sigmoidal-logistic approach, then estimated the hazardous concentration for 5 % of species (HC5) using OriginPro8
software (Wu et al. 2013). CMCs were obtained by halving
the HC5 values following the US EPA water quality criteria
guidelines (Stephan et al. 1985).
QICARs to predict the toxicity of 34 metals
Table 1 One-variable regression model with σp
Species
Predicting equations
n
r2
RSS
F
P
Mysidopsis bahia
Log 96 h-LC50 =(39.46±9.74) σp+(−3.67±1.04)
7
0.77
1.61
16.42
0.98×10−2
Data source
−2
Lussier et al. (1985)
C. virginica
Log 48 h-LC50 =(50.52±12.30) σp+(−4.73±1.33)
8
0.74
3.79
16.87
0.63×10
Calabrese et al. (1973)
F. heteroclitus
Mya arenaria
A. forbesi
P. longicarpus
Nereis virens
Nassarius obsoletus
Log 96
Log 96
Log 96
Log 96
Log 96
Log 96
h-LC50 =(45.95±9.45) σp+(−1.97±0.96)
h-LC50 =(49.83±12.00) σp+(−2.82±1.22)
h-LC50 =(57.81±12.33) σp+(−3.66±1.25)
h-LC50 =(44.26±18.26) σp+(−3.09±1.85)
h-LC50 =(39.44±14.93) σp+(−2.34±1.51)
h-LC50 =(15.42±5.07) σp+(1.27±0.51)
5
5
5
5
5
5
0.89
0.85
0.88
0.66
0.70
0.76
0.68
1.09
1.15
2.53
1.69
0.20
23.63
17.25
21.99
5.88
6.98
9.25
1.66×10−2
2.54×10−2
1.83×10−2
9.38×10−2
7.75×10−2
5.58×10−2
Eisler and Hennekey (1977)
Eisler and Hennekey (1977)
Eisler and Hennekey (1977)
Eisler and Hennekey (1977)
Eisler and Hennekey (1977)
Eisler and Hennekey (1977)
r2 is coefficient of correlation, RSS is residual sum of squares, and P is the statistical significance level
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Fig. 1 Regression models of log-LC50 and softness index (σp) for eight
model organisms
effects of tested metal, QICAR provides a potentially important field, both for academics and for those who are involved
in setting water quality criteria for metals, especially data-poor
metals.
Species sensitivity and HC5 derivation
Based on the above QICAR equations, we obtain the log-LC50
values of each marine species for 34 metals or metalloids
(Supplementary Table S2). It has been demonstrated that
C. virginica and Mysidopsis bahia are more sensitive to metals
than those of other phyla. C. virginica is most sensitive to Au,
Hg, Ag, and Cd, whereas Mysidopsis bahia exhibited increasing sensitivity from soft to hard metals with log-LC50 values
ranging from 0.16 to 3.55. These metals or metalloids
belonged to IIA (Be, Mg, Ca, Sr, Ba), IIIA (Al, Ga, In, Tl),
IVA (Ge, Sn, Pb), VA (As, Sb, Bi), IB (Cu), IIB (Zn), IIIB (Sc,
Y, La), IVB (Ti), VB (V), VIB (Cr), VIIB (Mn), and VIII (Fe,
Co, Ni) with the σp>0.97×10−1. This result is consistent with
previous findings by Eisler and Hennekey (1997) and Snell
et al. (1991). Eisler and Hennekey (1997) conducted investigations on toxicological hazards of selected heavy metals to
representative marine species and exhibited that bivalve mollusks have the greatest sensitivity to Hg. Based on the comparative sensitivities for marine toxicity tests, Mysidopsis
bahia appeared to be more sensitive to Pb than other species
(Snell et al. 1991). Bivalves, especially the Mytilus sp., have
been recognized as species more sensitive to Cu (US EPA
2003). In the present study, log-LC50 of C. virginica (0.52)
was slightly greater than that of Mysidopsis bahia (0.44).
Besides, two bivalve mollusks, commonly served as marine pollution indicators, with different life stages are analyzed
in this study, where C. virginica was at the embryonic development stage and Mya arenaria at the mature stage. The
predicted LC50 values of C. virginica are significantly less
than those of Mya arenaria (Supplementary Table S2).
Environ Sci Pollut Res (2015) 22:4297–4304
Previously, it has been demonstrated that organisms had significantly greater sensitivities to toxicants in the embryonic
and juvenile stages than in the mature stage. Juvenile Ostrea
edulis were 1,000-fold more sensitive to Hg than were mature
adults (Connor 1972).
Additionally, comparisons have been made between the
predicted log-LC50 values and data from literature for those
metals with few toxicity data. For Ca, for example, the 48-h
excess log-LC50 of Mysidopsis bahia at salinity of 20‰ was
4.11 by Pillard et al. (2002), quite similar to the value predicted in this study (3.47). Thus, there is limited support for
concluding that predicted CMCs based on these toxicity data
would provide reference values for those metals without
criteria value.
By using predicted acute toxicity values as the x-axis and
cumulative probability as the y-axis, SSD plots of cumulative
probabilities are used to calculate HC5 values for 34 metals or
metalloids (Fig. 2). These curves have r2 of >0.91, F of
>1.13×102, and P of <8.07×10−5, which indicate a good
fitting of the sigmoidal-logistic model (Supplementary
Table S3). Log-HC5 values of soft ions are less than those of
boundary ions and hard ions. Specifically, soft ions, such as
Hg, Ag, and Cd, have greater toxic potencies than boundary
ions, such as Cu, Zn, Ni, and Pb, and hard ions, including Ca
and Mg. These findings indicate that potencies of metal ions to
cause toxicity are mainly attributed to their covalent binding
with S- and N-containing groups in biological molecules.
Results of previous studies have shown that binding constants
for metal-ligand complexes, as well as the amount of metallothionein produced, were closely related to σp (Couillard
et al. 1993; Zhou et al. 2011). Under the stress of exposure
to harmful metals, soft ions, such as Hg and Cd, can rapidly
Fig. 2 Species sensitivity distribution analysis and derivation of the
predicted log-HC5 based on the QICAR regressions for 34 metals or
metalloids. The predicted toxicities were derived from a minimum of
eight species (three phyla), including Mysidopsis bahia, C. virginica,
F. heteroclitus, Mya arenaria, A. forbesi, P. longicarpus, Nereis virens,
and Nassarius obsoletus
Environ Sci Pollut Res (2015) 22:4297–4304
4301
bind with S-containing groups (−SH and C-SH) in biological
organisms, further inducing the synthesis of metallothionein
and reducing the enzyme activity or causing damage to structural proteins on the cytoplasm and cell membranes of organisms. Criteria maximum concentrations (CMCs) were recently
derived for freshwater organisms by use of the QICAR-SSD
model (Wu et al. 2013). The same ion characteristics were
used in derivation of both freshwater and marine WQC.
Predicted log-HC5 values of 34 metals were strongly correlation with σp (Fig. 3, r2 =0.97, F=1.01×103, P=0.10×10−3),
which indicates that to some extent, it is feasible to derive
WQC for marine organisms using the available toxicity data
of freshwater organisms.
Validation and applicability of the QICAR-SSD model
QICAR-SSD models produced predicted CMCs for marine
organisms, which were then compared with CMCs recommended by the US EPA for eight metals, and relative standard
deviations between values predicted by use of the QICARSSD method and those for which values had been promulgated by the US EPA were as follows: Hg<Ni<Zn<As (III)<Ag
<Cd<Cu<Pb. Deviations between predicted and promulgated values for Hg, Ni, Zn, As (III), and Ag were within a
difference of 0.5 orders of magnitude, whereas those for Cd,
Cu, and Pb are within a factor of 10 (Fig. 4). Acute toxicities
of six metals, Hg, Cd, Cu, Zn, Ni, and Pb, to the crustacean
Neomysis integer have been determined at different salinities.
Values of the 96 h LC50 for Hg were almost the same at
salinities ranging from 5 to 25‰; toxicities of Cd and Pb to
Neomysis integer were significantly inversely proportional to
salinity when compare to the effect of salinity on the toxicity
of Ni, Cu, and Zn (Verslycke et al. 2003), which was consistent with our predicting error. The combined QICAR-SSD
model provided weaker accurate prediction to metals with
toxicity easily affected by salinity.
Fig. 3 The model for log-HC5 and softness index (σp) at 95 % prediction
level
Fig. 4 The relationships between predicted log-HC5 and recommended
log-HC5 derived from WQC
Environmental factors, such as salinity and dissolved organic matter, have been observed to have strong effects on the
chemical speciation of Cd (Sunda et al. 1978; Endel and
Fowler 1979). In derivation of WQC for Cd in seawater,
effects of salinity on the toxicity of Cd to marine organisms
were discussed. While the toxic effects of Cd on most aquatic
species were found to be inversely proportional to salinity,
others (Voyer 1975) have found different results when studying effects of salinity on toxicity of Cd to the small euryhaline
fish, F. heteroclitus. Thus, salinity was not introduced as a
correction factor in the development of WQC for Cd in
seawater (US EPA 2001). For Cu, the predicted CMC was
9.19 μg/L, approximately twofold larger than the value recommended by the US EPA (4.8 μg/L). However, during the
derivation of CMC for saltwater organisms in EPA, final acute
value (FAV) was lessened from initially calculated value
(12.3 μg/L) to 6.19 μg/L for protecting commercially and
important mussel species (US EPA 2003). Consequently, a
more accurate prediction of toxicity of Cu in seawater would
be half of the calculated FAV. The QICAR-SSD model provided the least accurate prediction of toxicities of Pb, with a
predicted value of 507 μg/L, which is greater than the recommended value (210 μg/L). The US EPA used 13 sets of
toxicity data (nine invertebrate species and four fish species)
for derivation of WQC for Pb in seawater. Of these,
F. heteroclitus is the most sensitive species and has a significantly different sensitivity to Pb relative to the other three fish
species (US EPA 1980). LC50 values for the other three fishes
(Cyprinodon variegatus, Menidia beryllina, Menidia
menidia) all exceeded solubility of Pb in seawater under the
test conditions (3.14 × 10 3 μg/L, based on [Pb 2+ ] in
Pb(NO3)2). Moreover, fish also demonstrate large variances
during the derivation of water quality objective (WQO) for Pb
in Hong Kong (HKEPD 2012). Yet, F. heteroclitus appreared
to be less sensitive to other metals, like Cu and Cd, than those
marine fish mentioned above ((US EPA 2003; Middaugh and
4302
Dean 1977). Therefore, future studies need to better characterize the toxic effect and associated toxicity mechanisms of
Pb on marine organisms.
Comparison of predicted freshwater and marine WQC
When a dual-parameter QICAR-SSD model was used to
predict CMCs for metals in freshwater, CMCs for 25 metals
or metalloids were within 1.5 orders of magnitude (Wu et al.
2013). WQC derived for marine systems were generally within 1 order of magnitude, thus improving the prediction accuracy of the QICAR-SSD model. For this reason, toxicity tests
for seven model species were under the same condition.
However, for certain metals, accuracy was less than that
obtained by Wu et al. (2013). This is possibly due to complex
interactions among multiple factors (form of metal, environment factors, condition of the targeted organism) in the marine
environment (Bryan 1971). Metals, such as Pb, Cd, and Zn,
which favor complexation with chloride at greater salinity,
would have lesser concentrations of free ions of these metals.
Alternatively, the ionic state has the greatest bioavailability
and biological toxicity to aquatic organisms. Thus, these
metals usually exhibit significant inverse relationships with
salinity (Verslycke et al. 2003; Bielmyer et al. 2012). In
contrast to these metals, inorganic complexation seems to
have little importance in defining toxicity of Hg and Cu.
Differences exist between their toxicity with the changing
salinity. While acute toxicity of Hg is almost the same regardless of salinity, toxicity of Cu varied among salinities, probably by disrupting osmoregulation of the marine organism
(Grosell et al. 2007; Adeyemi et al. 2012). This has challenged
the prediction of metal toxicity to marine organisms using σp,
which can only characterize the physicochemical properties of
the metal itself.
Conclusions
Here, we have explored relationships between the marine
acute toxicity endpoints (LC50) and individual metal ion characteristics for 34 metals or metalloids. Using the resulting
QICARs, acute toxicities of each metal in saltwater were used
to develop a SSD for deriving 5 % hazard concentration (HC5)
values and CMCs for them. Then, we compared the predicted
values with the eight published metal CMCs recommended by
the US EPA (2009). Application of the QICAR-SSD method
allows screening CMCs prediction for metals in saltwater with
prediction errors within an order of magnitude. Thus, it might
serve as a reference for the development of WQC for marine
organisms in the absence of a CMC for metal and associated
ecological-environmental risk assessment. Furthermore, there
remains several points needing to be further developed: (1)
Environ Sci Pollut Res (2015) 22:4297–4304
Although it is not feasible to conduct toxicity testing for all
species and conditions, more scientific data, especially of
additional representative species, are needed for improving
the prediction accuracy(Wu et al. 2012; Warne et al. 2014). (2)
Salinity correction should be considered due to significant
effect on the prediction accuracy. (3) The feasibility of
QICAR-SSD model for prediction of chronic toxicity on
marine organisms should be discussed in the next stage.
Acknowledgments The present study was supported by the National
Basic Research Program of China (973 Program) (No. 2008CB418200),
the National Natural Science Foundation of China (No.U0833603 and
41130743), and the National Water Pollution Control and Management
Technology Major Projects of China (2012ZX07503- 003).
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1
Supplementary Material
2
3
Derivation of Marine Water Quality Criteria for Metals based on a Novel QICAR-SSD Model
4
5
Cheng Chena,b, Yunsong Mub, Fengchang Wua,b*, Ruiqing Zhangb, Hailei Sub, John P Giesyc,d
6
7
a College of Environmental Science and Engineering, Hohai University, Nanjing 210098, China
8
b State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of
9
Environmental Sciences, Beijing 100012, China
10
c Department of Veterinary Biomedical Sciences, University of Saskatchewan, Saskatoon, SK, Canada
11
d Department of Zoology, and Center for Integrative Toxicology, Michigan State University, East Lansing, MI,
12
USA
13
*
14
Telephone number: +86-10-84915312; Fax number: +86-10-84931804
15
E-mail: wufengchang@vip.skleg.cn.
To whom correspondence may be addressed
16
17
18
19
20
21
22
23
24
25
Number of pages: 4
26
Journal: Environ Sci Pollut Res
1
1
2
3
Table S1. log-LC50 of ten metal ions to eight taxonomic families used in regression models (μM)
Species
Cd
Cu
Pb
Hg
Ni
M. bahia
-0.009
0.455
1.179
-1.758
0.937
0.210
1.073
-1.554
1.303
C. virginica
Ag
-1.269
Zn
As(III)
0.883
1.366
0.676
2.000
Cr(VI)
2.464
F. heteroclitus
2.292
0.601
3.776
2.963
3.243
M. arenaria
1.347
0.300
3.737
2.071
3.040
A. forbesi
1.800
-0.524
3.408
2.776
2.789
P. longicarpus
1.063
-0.603
2.904
0.787
2.284
N. virens
1.918
-0.457
2.629
2.093
1.585
N. obsoletua
2.493
2.203
3.089
2.883
3.305
4
5
2
Mn
1
2
Table S2．Acute toxicities of 34 metals or metalloids to representative species from eight taxonomic families
Metals
σp
M.bahia
C.virginica
F.heteroclitus
M. arenaria
A.forbesi
Au
0.044
-1.932
-2.507
0.0570
-0.632
-1.118
-1.138
-0.608
1.950
Hg
0.065
-1.103
-1.446
1.022
0.415
0.096
-0.209
0.220
2.274
Ag
0.074
-0.748
-0.991
1.435
0.863
0.616
0.189
0.575
2.412
Cd
0.081
-0.472
-0.637
1.757
1.212
1.021
0.499
0.852
2.520
Tl
0.097
0.160
0.171
2.492
2.009
1.946
1.207
1.483
2.767
P. longicarpus
N.virens
N.obsoletua
Ga(III)
0.099
0.239
0.272
2.584
2.109
2.061
1.296
1.561
2.798
In(III)
0.1
0.278
0.322
2.630
2.159
2.120
1.340
1.601
2.813
Cu
0.104
0.436
0.524
2.814
2.358
2.350
1.517
1.759
2.875
As(III)
0.106
0.515
0.625
2.906
2.458
2.466
1.605
1.838
2.906
Cr(III)
0.107
0.554
0.676
2.951
2.507
2.524
1.650
1.877
2.921
Bi(III)
0.113
0.791
0.979
3.227
2.806
2.871
1.915
2.114
3.014
Zn
0.115
0.870
1.080
3.319
2.906
2.986
2.004
2.193
3.045
Sb
0.119
1.028
1.282
3.503
3.105
3.217
2.181
2.350
3.106
V(III)
0.12
1.067
1.333
3.549
3.155
3.275
2.225
2.390
3.122
Mn
0.125
1.265
1.585
3.778
3.404
3.564
2.446
2.587
3.199
Ni
0.126
1.304
1.636
3.824
3.454
3.622
2.491
2.626
3.214
Ti(III)
0.127
1.344
1.686
3.870
3.504
3.680
2.535
2.666
3.230
Fe(II)
0.129
1.422
1.787
3.962
3.604
3.796
2.623
2.745
3.260
Co
0.13
1.462
1.838
4.008
3.653
3.853
2.668
2.784
3.276
Pb
0.131
1.501
1.888
4.054
3.703
3.911
2.712
2.824
3.291
Al
0.136
1.699
2.141
4.284
3.952
4.200
2.933
3.021
3.368
Ge
0.138
1.778
2.242
4.376
4.052
4.316
3.022
3.100
3.399
V(II)
0.139
1.817
2.293
4.422
4.102
4.374
3.066
3.139
3.415
Sc(III)
0.14
1.856
2.343
4.468
4.152
4.431
3.110
3.179
3.430
Y(III)
0.147
2.133
2.697
4.789
4.501
4.836
3.420
3.455
3.538
Sn
0.148
2.172
2.747
4.835
4.550
4.894
3.464
3.494
3.553
Ti(II)
0.157
2.527
3.202
5.249
4.999
5.414
3.863
3.849
3.692
Mg
0.167
0.169
2.922
3.707
5.708
5.497
5.992
4.305
4.244
3.846
3.001
3.808
5.800
5.597
6.108
4.394
4.322
3.877
Sc(II)
La
0.171
0.172
3.080
3.909
5.892
5.696
6.224
4.482
4.401
3.908
3.119
3.960
5.938
5.746
6.281
4.526
4.441
3.923
Sr
0.174
3.198
4.061
6.030
5.846
6.397
4.615
4.520
3.954
Ca
0.181
0.183
3.474
4.414
6.351
6.195
6.802
4.925
4.796
4.062
3.553
4.515
6.443
6.294
6.917
5.013
4.875
4.093
Be
Ba
3
1
Table S3. SSD fitting parameters and CMCs derived for 34 metals, with coefficient of
2
determination, Chi-Sqr, F and P values.
3
Metals
a
Xc
k
a-SE
Xc-SE
k-SE
Chi-Sqr
Adj.r2
F
P
Au
0.951
0.059
-0.950
0.110
1.739
0.305
0.00348
0.963
253.051
266.952
Hg
Ag
Cd
Tl
Ga(III)
In(III)
0.972
0.997
1.031
1.392
1.551
1.658
0.066
0.086
0.094
0.403
0.617
0.795
0.080
0.542
0.922
2.169
2.446
2.608
0.114
0.146
0.155
0.529
0.729
0.877
1.718
1.617
1.550
1.187
1.122
1.089
0.315
0.331
0.289
0.280
0.290
0.298
0.0033
0.965
0.00385
0.959
0.00294
0.969
0.00274
0.971
0.00287
0.969
0.00298
0.968
228.076
299.017
321.081
307.202
295.468
log-HC5
AW
P-CMCs
CMCs
9.47×10-6
-2.613
197
0.240
/
-6
-1.616
201
2.431
1.8
-5
-1.278
107
2.822
1.9
-6
-0.999
112
5.617
40
-6
-0.603
204
25.455
/
-6
-0.586
70
9.080
/
-6
-0.580
115
15.140
/
-5
8.29×10
1.22×10
6.26×10
5.25×10
5.85×10
6.45×10
Cu
2.373
2.768
3.394
2.058
0.975
0.361
0.00399
0.957
220.466
1.33×10
-0.542
64
9.186
4.8
As(III)
2.768
4.692
3.728
2.909
0.949
0.407
0.0048
0.949
182.868
2.11×10-5
-0.484
75
12.297
69
200.693
-5
-0.533
52
7.614
/
-5
-0.334
209
48.393
/
-5
-0.268
65
17.516
90
-5
-0.108
51
19.904
/
-5
-0.059
51
22.274
/
-6
0.250
55
48.915
/
-6
0.319
59
61.491
74
-6
0.390
48
58.879
/
-6
0.537
56
96.371
/
-6
0.613
59
120.963
/
-6
0.690
207
507.178
210
-6
1.082
27
163.156
/
-5
1.233
73
624.098
/
-5
1.305
51
515.241
/
-5
1.376
45
534.444
/
-5
1.716
89
2314.408
/
-5
1.744
119
3296.985
/
-5
Cr(III)
Bi(III)
Zn
Sb
V(III)
Mn
Ni
Ti(III)
Fe(II)
Co
Pb
Al
Ge
V(II)
Sc(III)
Y(III)
Sn
4.399
494.2
332.4
25.00
26.09
5.153
4.110
3.384
2.469
2.170
1.938
1.316
1.196
1.151
1.113
0.998
0.995
13.487
201760
101629
571.9
568.2
12.979
7.544
4.735
2.245
1.663
1.280
0.502
0.388
0.348
0.316
0.245
0.249
4.516
10.89
10.53
7.467
7.556
5.490
5.189
4.929
4.506
4.334
4.186
3.720
3.633
3.605
3.586
3.672
3.705
4.965
502.4
379.5
30.87
29.26
3.953
2.973
2.330
1.582
1.355
1.181
0.693
0.586
0.544
0.508
0.443
0.456
0.884
0.819
0.815
0.820
0.821
0.883
0.903
0.925
0.977
1.007
1.039
1.225
1.305
1.344
1.383
1.504
1.498
0.388
0.364
0.404
0.418
0.384
0.270
0.262
0.259
0.266
0.277
0.290
0.392
0.443
0.470
0.497
0.671
0.693
0.00438
0.953
0.00402
0.957
0.0051
0.946
0.00567
0.940
0.0048
0.949
0.00227
0.976
0.00209
0.978
0.00199
0.979
0.002
0.979
0.0021
0.978
0.00224
0.976
0.00349
0.963
0.00417
0.956
0.00453
0.952
0.00491
0.948
0.00775
0.917
0.00823
0.912
218.539
171.822
154.621
182.743
387.695
422.100
443.511
440.533
420.267
392.966
251.772
210.913
193.799
178.544
112.515
105.915
1.68×10
1.36×10
2.46×10
3.19×10
2.12×10
3.29×10
2.66×10
2.35×10
2.39×10
2.69×10
3.18×10
9.59×10
1.49×10
1.83×10
2.24×10
6.97×10
8.07×10
Ti(II)
0.890
0.116
3.853
0.229
1.849
0.784
0.00768
0.918
113.670
6.79×10
2.326
48
5087.346
/
Mg
0.853
0.071
4.156
0.151
2.098
0.753
0.0066
0.930
132.413
4.67×10-5
2.833
24
8166.035
/
126.264
-5
2.857
45
16183.130
/
-5
2.867
139
51132.240
/
-5
2.911
4
1628.283
/
-5
2.984
88
42446.580
/
-5
3.150
40
28276.780
/
-5
3.183
137
104313.50
/
Sc(II)
La
Be
Sr
Ca
Ba
0.857
0.864
0.861
0.859
0.865
0.870
0.076
0.084
0.08
0.074
0.071
0.073
4.243
4.338
4.371
4.444
4.738
4.831
0.166
0.189
0.180
0.169
0.171
0.179
2.006
1.896
1.908
1.908
1.758
1.697
0.735
0.717
0.695
0.652
0.533
0.509
0.00692
0.926
0.00734
0.922
0.007
0.925
0.00641
0.932
0.00538
0.943
0.00531
0.943
4
4
118.916
124.899
136.403
162.871
165.214
5.25×10
6.08×10
5.39×10
4.35×10
2.81×10
2.71×10